SEVzero offers an end‑to‑end monitoring platform that records model metadata, code versions, and data lineage throughout the AI lifecycle, linking performance drift to specific pipeline changes. The system provides real‑time alerts, governance policy enforcement, and traceability reports via a centralized dashboard, helping enterprise MLOps and risk teams quickly identify and remediate hidden logic issues before they cause critical incidents.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprise AI pipelines often lack visibility into model logic and code changes, making it difficult to identify the root cause of production failures. This opacity leads to severe incidents, compliance breaches, and loss of trust in AI systems.
Solution
SEVzero provides an end‑to‑end monitoring platform that captures and stores model metadata, code versions, and data lineage across the entire AI lifecycle. By continuously analyzing performance drift and correlating anomalies with specific code or data modifications, the platform surfaces hidden logic issues before they trigger SEV‑0 incidents. Integrated governance policies enforce compliance rules and generate actionable alerts for engineers and risk teams. All insights are presented through a centralized metastore and dashboard, enabling rapid root‑cause analysis and preventive remediation.
Target Audience
Primary customers are AI engineering and MLOps teams in large enterprises that deploy mission‑critical machine‑learning models, as well as compliance and risk officers responsible for AI governance.
Features
- Comprehensive model metastore with full end‑to‑end lineage tracking of data, code, and configuration changes
- Real‑time performance drift analyzer that detects deviations and links them to specific pipeline events
- Governance policy engine that enforces compliance rules and automatically flags violations
- Automated traceability reports that map failures to their originating data or code artifacts
- Centralized dashboard for monitoring, alerting, and collaborative incident investigation